搜索结果: 1-6 共查到“统计学 Exploration”相关记录6条 . 查询时间(0.045 秒)
Best arm identification via Bayesian gap-based exploration
Best arm identification Bayesian gap-based exploration
2013/4/28
Bayesian approaches to optimization under bandit feedback have recently become quite popular in the machine learning community. Methods of this type have been found to have not only very good empirica...
Deterministic Sequencing of Exploration and Exploitation for Multi-Armed Bandit Problems
Deterministic Sequencing Exploration Exploitation Multi-Armed Bandit Problems
2011/7/7
In the Multi-Armed Bandit (MAB) problem, there are a given set of arms with unknown reward distributions. At each time, a player selects one arm to play, aiming to maximize the total expected reward o...
PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off
coherent framework PAC-Bayesian Analysis Exploration-Exploitation Trade-off
2011/6/21
We develop a coherent framework for integrative
simultaneous analysis of the explorationexploitation
and model order selection tradeoffs.
We improve over our preceding results
on the same subject ...
Free energy methods for efficient exploration of mixture posterior densities
Adaptive Biasing Force Adaptive Biasing Potential Adaptive Markov chainMonte Carlo Importance sampling Mixture models
2010/3/11
Because of their multimodality, mixture posterior densities are difficult to sample with
standard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhance
the sampling of MCMC in th...
Evolutionary Stochastic Search for Bayesian model exploration
Evolutionary Monte Carlo Fast Scan Metropolis-Hastings schemes Linear Gaussian regressionmodels Variable selection
2010/3/10
Implementing Bayesian variable selection for linear Gaussian regression models for analysing
high dimensional data sets is of current interest in many fields. In order to make such analysis operation...
Tree Exploration for Bayesian RL Exploration
Exploration Bayesian RL Exploration simpler baseline algorithm
2010/3/18
Research in reinforcement learning has produced algorithms for optimal
decision making under uncertainty that fall within two main types.
The first employs a Bayesian framework, where optimality imp...